{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ecom_submit.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyk191uf5DDa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install ppscore\n",
        "!pip install catboost\n",
        "!pip install sweetviz\n",
        "!pip install --upgrade pandas\n",
        "!pip install --upgrade numpy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cm9TBRF6KWJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2d8207f2-6508-415e-f885-54fd50b0a829"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import ppscore as pps\n",
        "import pickle\n",
        "from IPython.display import clear_output\n",
        "import sweetviz\n",
        "import datetime\n",
        "import pandas as pd\n",
        "from pandas_datareader import data\n",
        "import warnings\n",
        "from sklearn.model_selection import KFold, cross_val_score,StratifiedKFold\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder, label_binarize\n",
        "import csv\n",
        "import re\n",
        "from lightgbm import plot_importance,LGBMRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "from math import sqrt\n",
        "from catboost import CatBoostRegressor\n",
        "from catboost import Pool, cv\n",
        "import pickle\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from google.colab import drive\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "drive.mount(\"/GD\")"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /GD; to attempt to forcibly remount, call drive.mount(\"/GD\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_IdeDEmCSTB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "parse_dates = ['Date']\n",
        "df_train = pd.read_csv(\"/..Train.csv\",parse_dates=parse_dates)\n",
        "df_test = pd.read_csv(\"/..test.csv\",parse_dates=parse_dates)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkrYZxXsJ5OP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#building up the target customer column,about 435 products were manually labeled by the following code below\n",
        "train_prods=df_train['Subcategory_2'].tolist()\n",
        "test_prods=df_test['Subcategory_2'].tolist()\n",
        "prods=train_prods+test_prods\n",
        "prods=list(set(prods))\n",
        "\n",
        "# classifying target customer. Looking at the product I classified them into end user type women,men unisex,baby,kid\n",
        "d={}\n",
        "num=0\n",
        "for item in prods:\n",
        "  clear_output(wait=False)\n",
        "  print(str(num))\n",
        "  print(item)\n",
        "  i=input(\"classify \")\n",
        "  d[str(item)]=str(i)\n",
        "  num+=1\n",
        "  \n",
        "\n",
        "import pickle\n",
        "with open('/dict.p', 'wb') as fp:\n",
        "    pickle.dump(d, fp, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJOYTnu5_eOw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('...dict.p', 'rb') as fp:\n",
        "    x = pickle.load(fp)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFqeWrhA-dW7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "4cd42e44-69e4-4914-f5e2-cf222cda60a9"
      },
      "source": [
        "#a very powerful eda tool for getting an overview of the data\n",
        "my_report = sweetviz.compare([df_train.drop(['Date'],axis=1), \"Train\"], [df_test.drop(['Date'],axis=1), \"Test\"], \"Selling_Price\")"
      ],
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "                                   |                         | [  0%]   00:00  -> (? left)\u001b[A\n",
            "Summarizing dataframe:             |                         | [  0%]   00:00  -> (? left)\u001b[A\n",
            ":TARGET::                          |██▋                  | [ 12%]   00:00  -> (00:00 left)\u001b[A\n",
            ":TARGET::                          |█████▎               | [ 25%]   00:00  -> (00:01 left)\u001b[A\n",
            ":Product:                          |█████▎               | [ 25%]   00:00  -> (00:01 left)\u001b[A\n",
            ":Product_Brand:                    |███████▉             | [ 38%]   00:00  -> (00:01 left)\u001b[A\n",
            ":Product_Brand:                    |██████████▌          | [ 50%]   00:00  -> (00:00 left)\u001b[A\n",
            ":Item_Category:                    |██████████▌          | [ 50%]   00:00  -> (00:00 left)\u001b[A\n",
            ":Item_Category:                    |█████████████▏       | [ 62%]   00:01  -> (00:01 left)\u001b[A\n",
            ":Subcategory_1:                    |█████████████▏       | [ 62%]   00:01  -> (00:01 left)\u001b[A\n",
            ":Subcategory_2:                    |███████████████▊     | [ 75%]   00:01  -> (00:00 left)\u001b[A\n",
            ":Item_Rating:                      |██████████████████▍  | [ 88%]   00:01  -> (00:00 left)\u001b[A\n",
            ":Item_Rating:                      |█████████████████████| [100%]   00:02  -> (00:00 left)\u001b[A\n",
            ":FEATURES DONE:                    |█████████████████████| [100%]   00:02  -> (00:00 left)\n",
            "\n",
            "                                   |                         | [  0%]   00:00  -> (? left)\u001b[A\n",
            ":Processing Pairwise Features:     |                         | [  0%]   00:00  -> (? left)\u001b[A\n",
            ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Creating Associations graph... DONE!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxtoavKlHjv2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_report.show_html(\"Report.html\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsE8M22biJeU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#after the initial eda, two points that struck me was brand name and product name coudnt be used as feature as there were about 304 brands present in test \n",
        "#but not present in train. so i had to deal with this two columns in a separate way."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3NBHWjJiElb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#handling the datetime column\n",
        "df_train['Year'] = pd.to_datetime(df_train.Date).dt.year\n",
        "df_test['Year'] = pd.to_datetime(df_test.Date).dt.year\n",
        "df_train['Week'] = pd.to_datetime(df_train.Date).dt.week\n",
        "df_test['Week'] = pd.to_datetime(df_test.Date).dt.week\n",
        "df_train['day'] = pd.to_datetime(df_train.Date).dt.day\n",
        "df_test['day'] = pd.to_datetime(df_test.Date).dt.day\n",
        "df_train['Month'] = pd.to_datetime(df_train.Date).dt.month\n",
        "df_test['Month'] = pd.to_datetime(df_test.Date).dt.month\n",
        "festive={9:1,10:1,11:1,12:1}\n",
        "df_train[\"isfestive\"]=df_train.Month.map(festive)\n",
        "df_train[\"isfestive\"]=df_train[\"isfestive\"].fillna(0)\n",
        "df_train[\"isfestive\"]=df_train[\"isfestive\"].astype('int32')\n",
        "df_test[\"isfestive\"]=df_test.Month.map(festive)\n",
        "df_test[\"isfestive\"]=df_test[\"isfestive\"].fillna(0)\n",
        "df_test[\"isfestive\"]=df_test[\"isfestive\"].astype('int32')\n",
        "df_train['Qtr'] = pd.to_datetime(df_train.Date).dt.quarter\n",
        "df_test['Qtr'] = pd.to_datetime(df_test.Date).dt.quarter\n",
        "\n",
        "#major cleaning of item category was required before converting it to onehot. few unknowns in the subcategory 2 were actually present in item category\n",
        "#all the unknowns(4-5 maybe) were manually replaced with data from item category.\n",
        "#manual categorizing of item category below\n",
        "d_length1={'home improvement':'home_furnishing','home furnishing':'home_furnishing','be 13 printed boy s round neck t shirt pack of 2':'clothing',\n",
        "       'dressberry black synthetic clutch':'bike','sj bushnell 122 1000m binoculars 36 mm black':'camera','ruhi s creations polyester silk blend cartoon ki':'clothing','zikrak exim women wedges':'footwear','olvin wayfarer sunglasses':'eyewear','speedwav 216456 manual rear view mirror right':'automotive','olvin rectangular sunglasses':'eyewear','naaz 2 in 1 paper quilling board game':'kid','toys school supplies':'kid','asics gel kayano 22 running shoes':'footwear', 'remson india women flats':'footwear',\n",
        "       'clickforsign avoid contanimation wash your hands':'personal_care',\n",
        "       'easies solid single breasted casual men s blazer':'clothing_winter',\n",
        "       'kombee girl s printed red pink top capri set':'clothing',\n",
        "       'balaji exports bottled wine cooler 9 bottles':'kitchen',\n",
        "        'camey men s quarter length socks':'clothing',\n",
        "       'indistar self design viscose women s stole':'clothing','amita home furnishing cotton printed single beds':'home furnishing',\n",
        "       'bootwale bellies':'footwear','kittens boys flats':'footwear','baby care':'baby','jwellery':'jewellery','bags wallets belts':'clothing','home decor festive needs':'festive','miss wow slim fit women s blue jeans':'clothing',\n",
        "           'srpc baoer starwalker executive rollerball pen g':'office','pens stationery':'office','soie fashion women s sports bra':'sports',\n",
        "           'sports fitness':'sports','sunglasses':'eyewear','zevrr sterling silver swarovski zirconia platinu':'jwellery',\n",
        "           'adidas ind pro thi gua thigh guard white blue':'sports','arial morris women flats':'footwear', 'feet flow women flats':'footwear',\n",
        "       'sonaxo men running shoes':'footwear','say uv sterilizer solid filter cartridge 0 pac':'kitchen','home kitchen':'kitchen','kitchen dining':'kitchen',\n",
        "       'kalpaveda copper bowl gold pack of 1':'kitchen','tadd men s women s ankle length socks':'clothing',\n",
        "       'threads pals full sleeve self design men s swe':'clothing_winter',\n",
        "       'shonaya printed bhagalpuri art silk sari':'clothing',\n",
        "       'autoplus m ap15 arm sleeve black':'clothing',\n",
        "       'health personal care appliances':'health',\n",
        "       'frabjous german silver rings for women alloy zir':'jwellery',\n",
        "       'fabpoppy printed women s jumpsuit':'clothing','shopoj white paper sky lantern 80 cm x 34 cm p':'festive','home decor festive needs':'festive',\n",
        "       'killys men s solid no show socks':'clothing',\n",
        "       'sj barstel 56m 1000m binoculars 30 mm black':'camera',\n",
        "       'prime printed 8 seater table cover multicolor':'home furnishing',\n",
        "       'clovia women s t shirt bra':'clothing',\n",
        "       'royal seal creations 40 st color silver zircon n':'jewellery',\n",
        "       'libas printed women s anarkali kurta':'clothing',\n",
        "       'vishudh printed women s straight kurta':'clothing',\n",
        "       'e hiose girl s leggings pack of 6':'clothing', 'olvin aviator sunglasses':'eyewear',\n",
        "       'pout brass bangle':'jwellery_bangle',\n",
        "       'the cotton company solid women s polo neck pink':'clothing',\n",
        "       'dassler slim fit women s multicolor jeans':'clothing',\n",
        "       'masara solid women s straight kurta':'clothing','pu good women flats':'footwear',\n",
        "       'fly u slim fit fit women s brown jeans':'clothing',\n",
        "       'lilliput top baby girl s combo':'baby',\n",
        "       'favourite bikerz 3514 rad air filter ionic air f':'bike',\n",
        "       'mast harbour black synthetic clutch':'bike',\n",
        "       'speedwav 216552 manual rear view mirror right':'bike',\n",
        "       'laser x checkered men s boxer pack of 4':'clothing',\n",
        "       'newgen tech eo hs3303 218 wired headset white':'accessories',\n",
        "       'vishudh printed women s anarkali kurta':'clothing',\n",
        "       'coirfit single coir mattress':'home furnishing',\n",
        "       'ufo full sleeve solid girl s jacket':'clothing_winter',\n",
        "       'anuradha art stylish hair clip black':'personal_care', 'yo baby girl s trousers':'baby',\n",
        "       'd d women flats':'footwear', 'v g professional hd 37 hair dryer red':'accessories','beauty and personal care':'clothing','jwellery':'jewellery'}\n",
        "df_train[\"Item_Category\"]=df_train.Item_Category.replace(d_length1)\n",
        "df_test[\"Item_Category\"]=df_test.Item_Category.replace(d_length1)\n",
        "\n",
        "\n",
        "#below is target encoding of item and product based on selling price. now in normal circumstance this will lead to huge overfitting and target leak\n",
        "#but if the eda is seen carefully, the kind of product brand mix are almost same in test train. this means for a particular brand and product mix the price will only vary for time\n",
        "#thus this type of encoding done but not recomended otherwise.\n",
        "\n",
        "#############################################################################\n",
        "df=df_train[['Item_Category','Product_Brand','Selling_Price']]\n",
        "df = df.append(df_test[['Item_Category','Product_Brand','Selling_Price']])\n",
        "df=pd.DataFrame(df.groupby(['Item_Category','Product_Brand'])['Selling_Price'].mean())\n",
        "df=df.reset_index()\n",
        "df['brand_key']=df[['Item_Category', 'Product_Brand']].apply(lambda x: '_'.join(x), axis = 1)\n",
        "df=df.sort_values('Selling_Price')\n",
        "df['group'] = np.arange(len(df))\n",
        "brand_dict = dict(zip(df.brand_key, df.group))\n",
        "df_train['brand_key']=df_train[['Item_Category', 'Product_Brand']].apply(lambda x: '_'.join(x), axis = 1)\n",
        "df_test['brand_key']=df_test[['Item_Category', 'Product_Brand']].apply(lambda x: '_'.join(x), axis = 1)\n",
        "df_train['Class']=df_train.brand_key.replace(brand_dict)\n",
        "df_test['Class']=df_test.brand_key.map(brand_dict)\n",
        "\n",
        "\n",
        "###################################################################3\n",
        "df_train[\"Target_customer\"]=df_train.Subcategory_2.replace(x)\n",
        "df_test[\"Target_customer\"]=df_test.Subcategory_2.replace(x)\n",
        "df_test['Target_customer']=df_test.Target_customer.replace('','women')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TSG5Ku4kDF1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#the below function fills up the missing classes with human intervention. boxplot and rating is shown for reference for help in estimating class approx\n",
        "# import matplotlib.pyplot as plt\n",
        "prod=df_test.query('Class in [\"NaN\"]')['Subcategory_2'].tolist()\n",
        "item_cat=df_test.query('Class in [\"NaN\"]')['Item_Category'].tolist()\n",
        "rating=df_test.query('Class in [\"NaN\"]')['Item_Rating'].tolist()\n",
        "\n",
        "for item, rate, similar in zip(prod,rating,item_cat):\n",
        "    clear_output(wait=False)\n",
        "    print(\"similar in train class distribution as \\n\")\n",
        "    df_train.query(\"Item_Category in ['\"+str(similar)+\"']\").boxplot(column='Class')\n",
        "    plt.show()\n",
        "    print(\"product is \"+str(item))\n",
        "    print(\"rating of this prod is \"+str(rate))\n",
        "    i=input(\"assign an approx class\")\n",
        "    df_test.loc[df_test.Subcategory_2 == str(item), 'Class'] = str(i)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVHfkkRT7etU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c2ba4836-c7d4-4d21-b2d0-ff263f4cc1cc"
      },
      "source": [
        "###pps scores detect all types of relationship present in between variables. But if only linear relationship is of interest pearson corr matrix should still be used\n",
        "df=pps.matrix(df_train)\n",
        "######################################\n",
        "fig, ax = plt.subplots(figsize=(20,20)) \n",
        "# sns.set(font_scale=1.3)\n",
        "g=sns.heatmap(df, vmin=0, vmax=1, cmap=\"Reds\", linewidths=3, annot=True,ax=ax,annot_kws={\"fontsize\":10})"
      ],
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2000x2000 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sVCefuJFX7x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#as can be seen, i was successfull in bringing a high degree of correlation between product brand and class, while class and target_customer both appeared to be imp\n",
        "#thus we can drop product and product brand for further analysis"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTE8qdD9Hw-Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "1e0c77f5-4606-48d6-8629-15c7a52ca6b5"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Product_Brand</th>\n",
              "      <th>Item_Category</th>\n",
              "      <th>Subcategory_1</th>\n",
              "      <th>Subcategory_2</th>\n",
              "      <th>Item_Rating</th>\n",
              "      <th>Date</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Year</th>\n",
              "      <th>Week</th>\n",
              "      <th>day</th>\n",
              "      <th>Month</th>\n",
              "      <th>isfestive</th>\n",
              "      <th>Qtr</th>\n",
              "      <th>brand_key</th>\n",
              "      <th>Class</th>\n",
              "      <th>Target_customer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>P-2610</td>\n",
              "      <td>B-659</td>\n",
              "      <td>clothing</td>\n",
              "      <td>bags</td>\n",
              "      <td>hand bags</td>\n",
              "      <td>4.3</td>\n",
              "      <td>2017-02-03</td>\n",
              "      <td>291</td>\n",
              "      <td>2017</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>clothing_B-659</td>\n",
              "      <td>820</td>\n",
              "      <td>women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>P-2453</td>\n",
              "      <td>B-3078</td>\n",
              "      <td>clothing</td>\n",
              "      <td>women s clothing</td>\n",
              "      <td>western wear</td>\n",
              "      <td>3.1</td>\n",
              "      <td>2015-07-01</td>\n",
              "      <td>897</td>\n",
              "      <td>2015</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>clothing_B-3078</td>\n",
              "      <td>680</td>\n",
              "      <td>men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>P-6802</td>\n",
              "      <td>B-1810</td>\n",
              "      <td>festive</td>\n",
              "      <td>showpieces</td>\n",
              "      <td>ethnic</td>\n",
              "      <td>3.5</td>\n",
              "      <td>2019-01-12</td>\n",
              "      <td>792</td>\n",
              "      <td>2019</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>festive_B-1810</td>\n",
              "      <td>1121</td>\n",
              "      <td>men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>P-4452</td>\n",
              "      <td>B-3078</td>\n",
              "      <td>clothing</td>\n",
              "      <td>eye care</td>\n",
              "      <td>h2o plus eye care</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014-12-12</td>\n",
              "      <td>837</td>\n",
              "      <td>2014</td>\n",
              "      <td>50</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>clothing_B-3078</td>\n",
              "      <td>680</td>\n",
              "      <td>unisex</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>P-8454</td>\n",
              "      <td>B-3078</td>\n",
              "      <td>clothing</td>\n",
              "      <td>men s clothing</td>\n",
              "      <td>t shirts</td>\n",
              "      <td>4.3</td>\n",
              "      <td>2013-12-12</td>\n",
              "      <td>470</td>\n",
              "      <td>2013</td>\n",
              "      <td>50</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>clothing_B-3078</td>\n",
              "      <td>680</td>\n",
              "      <td>men</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Product Product_Brand Item_Category  ...        brand_key Class  Target_customer\n",
              "0  P-2610         B-659      clothing  ...   clothing_B-659   820            women\n",
              "1  P-2453        B-3078      clothing  ...  clothing_B-3078   680              men\n",
              "2  P-6802        B-1810       festive  ...   festive_B-1810  1121              men\n",
              "3  P-4452        B-3078      clothing  ...  clothing_B-3078   680           unisex\n",
              "4  P-8454        B-3078      clothing  ...  clothing_B-3078   680              men\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XCtHJxcFrff",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "X,Y=df_train.drop(['brand_key','Selling_Price','Date','Subcategory_2','Subcategory_1','Product','Product_Brand'],axis=1),df_train['Selling_Price']\n",
        "X=pd.get_dummies(X)\n",
        "X_test=df_test.drop(['brand_key','Date','Subcategory_2','Subcategory_1','Product','Product_Brand'],axis=1)\n",
        "X_test=pd.get_dummies(X_test)\n",
        "\n",
        "#prep for catboost \n",
        "numerics = ['int64','int32','object']\n",
        "df_train_cat= df_train.drop(['Product','Product_Brand','brand_key','Selling_Price'],axis=1).select_dtypes(include=numerics)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATHMjjNtLlwP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##after extensive feature selection\n",
        "gbc_columns=['Class','Target_customer', 'Item_Category_festive',\n",
        "       'Item_Category_furniture', 'Item_Category_footwear', 'Month',\n",
        "       'Item_Category_mobiles accessories', 'Item_Category_clothing',\n",
        "       'Item_Category_computers', 'Item_Category_pet supplies',\n",
        "       'Item_Category_home_furnishing',\n",
        "       'Item_Category_camera', 'Item_Category_baby',\n",
        "       'Item_Category_watches',\n",
        "       'Item_Category_home entertainment',\n",
        "       'isfestive']\n",
        "xgb_columns=['Class','Target_customer', 'Item_Category_jewellery',\n",
        "       'Item_Category_kitchen', 'Item_Category_footwear',\n",
        "       'Item_Category_clothing', 'Month',\n",
        "       'Item_Category_home_furnishing', 'Item_Category_computers']\n",
        "cat_columns=['Item_Category', 'Subcategory_1', 'Subcategory_2', 'Year', 'Month',\n",
        "       'Week', 'day', 'isfestive', 'Qtr',\n",
        "       'Target_customer', 'Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rfKhhXRyhIS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_GB=X[gbc_columns]\n",
        "X_XGB=X[xgb_columns]\n",
        "X_cat=df_train[df_train_cat.columns]\n",
        "X_GB_test=X_test[gbc_columns]\n",
        "X_XGB_test=X_test[xgb_columns]\n",
        "X_cat_test=df_test[df_train_cat.columns]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlBB_tmEx9dK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#hypertuned parameters. full grid was done for xgb and gbr while bayescv was done for catboost. code not included. this was done after feature selection.\n",
        "train_pool=Pool(df_train_cat,Y,cat_features=[0,1,2])\n",
        "catboost_model=CatBoostRegressor(bagging_temperature=0.617,border_count=198, \n",
        "depth=4, iterations=876,l2_leaf_reg=19,learning_rate=0.154, \n",
        "random_strength=0.003,cat_features=[0,1,2],verbose=0)\n",
        "gbc=GradientBoostingRegressor(learning_rate=0.05,max_depth=7, n_estimators=100, subsample=0.7)\n",
        "xgb=XGBRegressor(learning_rate=0.03,max_depth=7,min_child_weight=1,n_estimators=200,subsample=0.7,silent=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOHADHdoLCci",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f8f57b4d-802c-4cee-cccf-17514eca9f76"
      },
      "source": [
        "##catboost model test\n",
        "l=[]\n",
        "\n",
        "fold = StratifiedKFold(n_splits=10,random_state=True)\n",
        "scores=[]\n",
        "for train, test in fold.split(X_cat,Y):\n",
        "    x_train, x_test = X_cat.values[train], X_cat.values[test]\n",
        "    y_train, y_test = np.log(Y.values[train]), Y.values[test] \n",
        "    model = catboost_model\n",
        "    model.fit(x_train, y_train)\n",
        "    preds = np.exp(model.predict(x_test))\n",
        "    feature_importances = pd.DataFrame(model.feature_importances_,\n",
        "                                       index = X_cat.columns,\n",
        "                                        columns=['importance'])\n",
        "    sum=feature_importances.values\n",
        "    l.append(sum)\n",
        "    # preds[preds<0]=0\n",
        "    score = sqrt(mean_squared_log_error(y_test, preds))\n",
        "    scores.append(score)\n",
        "    print(score)\n",
        "print(\"Average scores are: \", np.sum(scores)/len(scores))"
      ],
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.3113131971507608\n",
            "0.5260382978370307\n",
            "0.5575112319463054\n",
            "0.4641511043324526\n",
            "0.5294980982105372\n",
            "0.5025909254517653\n",
            "0.4854011116412182\n",
            "0.5374081880950864\n",
            "0.5475865822619607\n",
            "0.4942594449708505\n",
            "Average scores are:  0.5955758181897968\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YLWogfpLuCH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "84a7336d-c8fe-4ea4-b2ba-88a172b636df"
      },
      "source": [
        "#identifying best average features from cv\n",
        "train_set=df_train_cat\n",
        "labels=Y\n",
        "add=0\n",
        "for item in l:\n",
        "  add+=item\n",
        "df_cv=pd.DataFrame(add/len(l),index=train_set.columns,columns=[\"importance\"]).sort_values('importance', ascending=False)\n",
        "df_cv=df_cv.reset_index()\n",
        "feat_imp = pd.Series(df_cv.importance.values, index=df_cv.drop([\"importance\"], axis=1)).sort_values(axis='index',ascending=False)\n",
        "# \n",
        "feat_imp2=feat_imp[feat_imp>0.00005]\n",
        "imp_columns=[]\n",
        "for item in pd.DataFrame(feat_imp2).reset_index()[\"index\"].tolist():\n",
        "  f=re.sub(\"[(),]\",\"\",str(item))\n",
        "  try:  \n",
        "    columns= int(re.sub(\"['']\",\"\",f))\n",
        "    imp_columns.append(columns)\n",
        "  except:\n",
        "    columns= re.sub(\"['']\",\"\",f)\n",
        "    imp_columns.append(columns)\n",
        "# X_UPDATED=df_train_cat[imp_columns]\n",
        "len(imp_columns)"
      ],
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2O2MVs9cMAAr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "5bb9dc16-822f-4076-90f5-bdad5ee9a78d"
      },
      "source": [
        "feat_imp.nlargest(5).plot(kind='barh', figsize=(5,5))\n"
      ],
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd64905fda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 316
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZR-mHkkrNZvE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "91e465c8-0354-47d1-f224-cb32b586e7e3"
      },
      "source": [
        "#testing for gradient boosting regressor\n",
        "l=[]\n",
        "\n",
        "fold = StratifiedKFold(n_splits=10,random_state=True)\n",
        "scores=[]\n",
        "for train, test in fold.split(X_GB,Y):\n",
        "    x_train, x_test = X_GB.values[train], X_GB.values[test]\n",
        "    y_train, y_test = np.log(Y.values[train]), Y.values[test] \n",
        "    model = gbc\n",
        "    model.fit(x_train, y_train)\n",
        "    preds = np.exp(model.predict(x_test))\n",
        "    feature_importances = pd.DataFrame(model.feature_importances_,\n",
        "                                       index = X_GB.columns,\n",
        "                                        columns=['importance'])\n",
        "    sum=feature_importances.values\n",
        "    l.append(sum)\n",
        "    # preds[preds<0]=0\n",
        "    score = sqrt(mean_squared_log_error(y_test, preds))\n",
        "    scores.append(score)\n",
        "    print(score)\n",
        "print(\"Average scores are: \", np.sum(scores)/len(scores))"
      ],
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.3447614380898902\n",
            "0.5295481773628165\n",
            "0.5388362314124157\n",
            "0.41331761635064623\n",
            "0.5048482107611507\n",
            "0.45335508940853253\n",
            "0.46342355885344044\n",
            "0.5878224144853352\n",
            "0.5402580785547298\n",
            "0.555226046644268\n",
            "Average scores are:  0.5931396861923225\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUd03iOdP8aG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "0d3e4e4b-6385-4b4b-a408-420d907da825"
      },
      "source": [
        "train_set=X_GB\n",
        "labels=Y\n",
        "add=0\n",
        "for item in l:\n",
        "  add+=item\n",
        "df_cv=pd.DataFrame(add/len(l),index=train_set.columns,columns=[\"importance\"]).sort_values('importance', ascending=False)\n",
        "df_cv=df_cv.reset_index()\n",
        "feat_imp = pd.Series(df_cv.importance.values, index=df_cv.drop([\"importance\"], axis=1)).sort_values(axis='index',ascending=False)\n",
        "# \n",
        "feat_imp2=feat_imp[feat_imp>0.00005]\n",
        "imp_columns=[]\n",
        "for item in pd.DataFrame(feat_imp2).reset_index()[\"index\"].tolist():\n",
        "  f=re.sub(\"[(),]\",\"\",str(item))\n",
        "  try:  \n",
        "    columns= int(re.sub(\"['']\",\"\",f))\n",
        "    imp_columns.append(columns)\n",
        "  except:\n",
        "    columns= re.sub(\"['']\",\"\",f)\n",
        "    imp_columns.append(columns)\n",
        "# X_UPDATED=X_GB[imp_columns]\n",
        "len(imp_columns)\n",
        "feat_imp.nlargest(5).plot(kind='barh', figsize=(5,5))\n"
      ],
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd63c39bf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 320
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pb38-xAzQZAc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "89b300a6-acd4-457c-ff77-c5dd7816c6d9"
      },
      "source": [
        "#testing for xgbregressor\n",
        "l=[]\n",
        "fold = StratifiedKFold(n_splits=10,random_state=True)\n",
        "scores=[]\n",
        "for train, test in fold.split(X_XGB,Y):\n",
        "    x_train, x_test = X_XGB.values[train], X_XGB.values[test]\n",
        "    y_train, y_test = np.log(Y.values[train]), Y.values[test] \n",
        "    model = xgb\n",
        "    model.fit(x_train, y_train)\n",
        "    preds = np.exp(model.predict(x_test))\n",
        "    feature_importances = pd.DataFrame(model.feature_importances_,\n",
        "                                       index = X_XGB.columns,\n",
        "                                        columns=['importance'])\n",
        "    sum=feature_importances.values\n",
        "    l.append(sum)\n",
        "    # preds[preds<0]=0\n",
        "    score = sqrt(mean_squared_log_error(y_test, preds))\n",
        "    scores.append(score)\n",
        "    print(score)\n",
        "print(\"Average scores are: \", np.sum(scores)/len(scores))"
      ],
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.44808729277513\n",
            "0.5338046744731467\n",
            "0.5227674149666978\n",
            "0.4390334710655028\n",
            "0.48064584690288537\n",
            "0.4509598915425695\n",
            "0.4577327542796703\n",
            "0.571754400229372\n",
            "0.5435546463769801\n",
            "0.48830895918835565\n",
            "Average scores are:  0.5936649351800309\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDAMOClhRGdN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_gbc=gbc.predict(X_GB_test.values)\n",
        "pred_xgb=xgb.predict(X_XGB_test.values)\n",
        "pred_cat=catboost_model.predict(X_cat_test.values)\n",
        "solution=pd.DataFrame()\n",
        "solution['gbc']=pred_gbc\n",
        "solution['xgb']=pred_xgb\n",
        "solution['cat']=pred_cat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpmLMfvn0-j2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "solution['Selling_Price']=0.2*solution['cat']+0.4*solution['xgb']+0.4*solution['gbc']\n",
        "solution['Selling_Price'].to_csv(\"finalsolution.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zFfvK5AFa6c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}